summary.npsf: 'summary' method for class 'npsf'

In npsf: Nonparametric and Stochastic Efficiency and Productivity Analysis

Description

Prints summary of SF or DEA model estimated by sf, teradial, tenonradial, and teradialbc, or testing procedures nptestrts and nptestind.

Usage

 ## S3 method for class 'npsf'
summary( object, ... )
 ## S3 method for class 'summary.npsf'
print( x, digits = NULL, print.level = NULL, ... )

Arguments

object	an object of class npsf returned by one of the functions sf, teradial, tenonradial, teradialbc, nptestrts or nptestind.
x	an object of class npsf returned by one of the functions sf, teradial, tenonradial, teradialbc, nptestrts or nptestind.
digits	numeric. Number of digits to be displayed in estimation results and for efficiency estimates. Default is 4.
print.level	numeric. 0 - nothing is printed; 1 - print summary of the model and data. 2 - print summary of technical efficiency measures. 3 - print estimation results observation by observation (for DEA models). Default is 1.
...	currently unused.

Details

The summary depends on the model or testing procedure that is being estimated

Value

Currently no value is returned

Author(s)

Oleg Badunenko <[e-mail]>

See Also

sf, teradial, tenonradial, teradialbc, tenonradialbc, nptestrts, and nptestind

Examples

require( npsf )
 
# Load Penn World Tables 5.6 dataset
 
data( pwt56 )
 
# Stochastic production frontier model with 
# homoskedastic error components (half-normal)
 
# Use subset of observations - for year 1965

# DEA

t1 <- teradialbc ( Y ~ K + L, data = pwt56, subset = Nu < 10, 
 reps = 199, print.level = 0)
summary(t1)

# SFA
 
m1 <- sf(log(Y) ~ log(L) + log(K), data = pwt56, 
 subset = year == 1965, distribution = "h",
 print.level = 0)
summary( m1 )

# Load U.S. commercial banks dataset
 
data(banks05)

m3 <- sf(lnC ~ lnw1 + lnw2 + lny1 + lny2, ln.var.u.0i = ~ ER, 
         ln.var.v.0i = ~ LA, data = banks05, distribution = "t", 
         prod = FALSE, print.level = 3)
 
summary(m3)

npsf documentation built on Nov. 23, 2020, 1:07 a.m.